Adaptive Ant Colony Optimization Algorithm for Hierarchical Scheduling Problem

O. Semenkina, E. Popov, O. Semenkina
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Abstract

The paper deals with the scheduling problem relevant in many fields, such as project management, lesson scheduling or production scheduling. We propose to implement a hierarchical problem structure that puts the travelling salesman problem at the top and replaces the nested resource-constrained project scheduling problem with a simulation model. The paper considers using adaptive parameters control method for Ant Colony Optimization. The performance comparison with such algorithms as Lin-Kernighan heuristic, Genetic Algorithm and Intelligent Water Drops Algorithm is made and competitive results are demonstrated.
层次调度问题的自适应蚁群优化算法
本文研究了项目管理、课程调度、生产调度等多个领域的调度问题。我们建议实现一个分层问题结构,将旅行推销员问题放在顶部,并用仿真模型取代嵌套的资源约束项目调度问题。本文考虑采用自适应参数控制方法进行蚁群优化。并与Lin-Kernighan启发式算法、遗传算法和智能水滴算法等算法进行了性能比较,给出了具有竞争力的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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